Estrogen regulation and functional role of FGFR4 in estrogen receptor positive breast cancer.

Background: Resistance to endocrine therapy is a major challenge of managing estrogen receptor positive (ER+) breast cancer. We previously reported frequent overexpression of FGFR4 in endocrine resistant cell lines and breast cancers that recurred and metastasized following endocrine therapy, suggesting FGFR4 as a potential driver of endocrine resistance. In this study, we investigated the role of FGFR4 in mediating endocrine resistance and explored the therapeutic potential of targeting FGFR4 in advanced breast cancer. Methods: A gene expression signature of FGFR4 activity was examined in ER+ breast cancer pre- and post-neoadjuvant endocrine therapy and the association between FGFR4 expression and patient survival was examined. A correlation analysis was used to uncover potential regulators of FGFR4 overexpression. To investigate if FGFR4 is necessary to drive endocrine resistance, we tested response to FGFR4 inhibition in long term estrogen deprived (LTED) cells and their paired parental cells. Doxycycline inducible FGFR4 overexpression and knockdown cell models were generated to examine if FGFR4 was sufficient to confer endocrine resistance. Finally, we examined response to FGFR4 monotherapy or combination therapy with fulvestrant in breast cancer cell lines to explore the potential of FGFR4 targeted therapy for advanced breast cancer and assessed the importance of PAM50 subtype in response to FGFR4 inhibition. Results: A FGFR4 activity gene signature was significantly upregulated post neoadjuvant aromatase inhibitor treatment, and high FGFR4 expression predicted poorer survival in patients with ER+ breast cancer. Gene expression association analysis using TCGA, METABRIC and SCAN-B datasets uncovered ER as the most significant gene negatively correlated with FGFR4 expression. ER negatively regulates FGFR4 expression at both the mRNA and protein level across multiple ER+ breast cancer cell lines. Despite robust overexpression of FGFR4, LTED cells did not show enhanced responses to FGFR4 inhibition compared to parental cells. Similarly, FGFR4 overexpression, knockdown or hotspot mutations did not significantly alter response to endocrine treatment in ER+ cell lines, nor did FGFR4 and fulvestrant combination treatment show synergistic effects. The HER2-like subtype of breast cancer showed elevated expression of FGFR4 and an increased response to FGFR4 inhibition relative to other breast cancer subtypes. Conclusions: Despite ER-mediated upregulation of FGFR4 post endocrine therapy, our study does not support a general role of FGFR4 in mediating endocrine resistance in ER+ breast cancer. Our data suggests that specific genomic backgrounds such as HER2 expression may be required for FGFR4 function in breast cancer and should be further explored.

Analytical validation of the 7-gene biosignature for prediction of recurrence risk and radiation therapy benefit for breast ductal carcinoma in situ.

Purpose: Ductal carcinoma in situ (DCIS), is a noninvasive breast cancer, representing 20-25% of breast cancer diagnoses in the USA. Current treatment options for DCIS include mastectomy or breast-conserving surgery (BCS) with or without radiation therapy (RT), but optimal risk-adjusted treatment selection remains a challenge. Findings from past and recent clinical trials have failed to identify a 'low risk' group of patients who do not benefit significantly from RT after BCS. To address this unmet need, a DCIS biosignature, DCISionRT (PreludeDx, Laguna Hills, CA), was developed and validated in multiple cohorts. DCISionRT is a molecular assay with an algorithm reporting a recurrence risk score for patients diagnosed with DCIS intended to guide DCIS treatment. In this study, we present results from analytical validity, performance assessment, and clinical performance validation and clinical utility for the DCISionRT test comprised of multianalyte assays with algorithmic analysis. Methods: The analytical validation of each molecular assay was performed based on the Clinical and Laboratory Standards Institute (CLSI) guidelines Quality Assurance for Design Control and Implementation of Immunohistochemistry Assays and the College of American Pathologists/American Society of Clinical Oncology (CAP/ASCO) recommendations for analytic validation of immunohistochemical assays. Results: The analytic validation showed that the molecular assays that are part of DCISionRT test have high sensitivity, specificity, and accuracy/reproducibility (≥95%). The analytic precision of the molecular assays under controlled non-standard conditions had a total standard deviation of 6.6 (100-point scale), where the analytic variables (Lot, Machine, Run) each contributed <1% of the total variance. Additionally, the precision in the DCISionRT test result (DS) had a 95%CI ≤0.4 DS units under controlled non-standard conditions (Day, Lot, and Machine) for molecular assays over a wide range of clinicopathologic factor values. Clinical validation showed that the test identified 37% of patients in a low-risk group with a 10-year invasive IBR rate of ~3% and an absolute risk reduction (ARR) from RT of 1% (number needed to treat, NNT=100), while remaining patients with higher DS scores (elevated-risk) had an ARR for RT of 9% (NNT=11) and 96% clinical sensitivity for RT benefit. Conclusion: The analytical performance of the PreludeDx DCISionRT molecular assays was high in representative formalin-fixed, paraffin-embedded breast tumor specimens. The DCISionRT test has been analytically validated and has been clinically validated in multiple peer-reviewed published studies.

Novel markers in breast pathology.

Breast pathology is an ever-expanding database of information which includes markers, or biomarkers, that detect or help treat the disease as prognostic or predictive information. This review focuses on these aspects of biomarkers which are grounded in immunohistochemistry, liquid biopsies and next-generation sequencing.

A Novel Biosignature Identifies Patients With DCIS With High Risk of Local Recurrence After Breast Conserving Surgery and Radiation Therapy.

PURPOSE: There is an unmet need to identify women diagnosed with ductal carcinoma in situ (DCIS) with a low risk of in-breast recurrence (IBR) after breast conserving surgery (BCS), which could omit radiation therapy (RT), and also to identify those with elevated IBR risk remaining after BCS plus RT. We evaluated a novel biosignature for a residual risk subtype (RRt) to help identify patients with elevated IBR risk after BCS plus RT. METHODS AND MATERIALS: Women with DCIS treated with BCS with or without RT at centers in the US, Australia, and Sweden (n = 926) were evaluated. Patients were classified into 3 biosignature risk groups using the decision score (DS) and the RRt category: (1) Low Risk (DS ≤2.8 without RRt), (2) Elevated Risk (DS >2.8 without RRt), and (3) Residual Risk (DS >2.8 with RRt). Total and invasive IBR rates were assessed by risk group and treatment. RESULTS: In patients at low risk, there was no significant difference in IBR rates with or without RT (total, P = .8; invasive IBR, P = .7), and there were low overall 10-year rates (total, 5.1%; invasive, 2.7%). In patients with elevated risk, IBR rates were decreased with RT (total: hazard ratio [HR], 0.25; P < .001; invasive: HR, 0.28; P = .005); 10-year rates were 20.6% versus 4.9% (total) and 10.9% versus 3.1% (invasive). In patients with residual risk, although IBR rates decreased with RT after BCS (total: HR, 0.21; P < .001; invasive: HR, 0.29; P = .028), IBR rates remained significantly higher after RT compared with patients with elevated risk (HR, 2.5; 95% CI, 1.2-5.4; P = .018), with 10-year rates of 42.1% versus 14.7% (total) and 18.3% versus 6.5% (invasive). CONCLUSIONS: The novel biosignature identified patients with 3 distinct risk profiles: Low Risk patients with a low recurrence risk with or without adjuvant RT, Elevated Risk patients with excellent outcomes after BCS plus RT, and Residual Risk patients with an elevated recurrence risk remaining after BCS plus RT, warranting potential intensified or alternative treatment approaches.

The Story of the Magee Equations: The Ultimate in Applied Immunohistochemistry.

Magee equations (MEs) are a set of multivariable models that were developed to estimate the actual Onco type DX (ODX) recurrence score in invasive breast cancer. The equations were derived from standard histopathologic factors and semiquantitative immunohistochemical scores of routinely used biomarkers. The 3 equations use slightly different parameters but provide similar results. ME1 uses Nottingham score, tumor size, and semiquantitative results for estrogen receptor (ER), progesterone receptor, HER2, and Ki-67. ME2 is similar to ME1 but does not require Ki-67. ME3 includes only semiquantitative immunohistochemical expression levels for ER, progesterone receptor, HER2, and Ki-67. Several studies have validated the clinical usefulness of MEs in routine clinical practice. The new cut-off for ODX recurrence score, as reported in the Trial Assigning IndividuaLized Options for Treatment trial, necessitated the development of Magee Decision Algorithm (MDA). MEs, along with mitotic activity score can now be used algorithmically to safely forgo ODX testing. MDA can be used to triage cases for molecular testing and has the potential to save an estimated $300,000 per 100 clinical requests. Another potential use of MEs is in the neoadjuvant setting to appropriately select patients for chemotherapy. Both single and multi-institutional studies have shown that the rate of pathologic complete response (pCR) to neoadjuvant chemotherapy in ER+/HER2-negative patients can be predicted by ME3 scores. The estimated pCR rates are 0%, <5%, 14%, and 35 to 40% for ME3 score <18, 18 to 25, >25 to <31, and 31 or higher, respectively. This information is similar to or better than currently available molecular tests. MEs and MDA provide valuable information in a time-efficient manner and are available free of cost for anyone to use. The latter is certainly important for institutions in resource-poor settings but is also valuable for large institutions and integrated health systems.

Deep learning models for histologic grading of breast cancer and association with disease prognosis.

Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.

A Consortium for Analytic Standardization in Immunohistochemistry.

CONTEXT.­: The authors announce the launch of the Consortium for Analytic Standardization in Immunohistochemistry, funded with a grant from the National Cancer Institute. As with other laboratory testing, analytic standards are important for many different stakeholders: commercial vendors of instruments and reagents, biopharmaceutical firms, pathologists, scientists, clinical laboratories, external quality assurance organizations, and regulatory bodies. Analytic standards are customarily central to assay development, validation, and method transfer into routine assays and are critical quality assurance tools. OBJECTIVE.­: To improve immunohistochemistry (IHC) test accuracy and reproducibility by integrating analytic standards into routine practice. To accomplish this mission, the consortium has 2 mandates: (1) to experimentally determine analytic sensitivity thresholds (lower and upper limits of detection) for selected IHC assays, and (2) to inform IHC stakeholders of what analytic standards are, why they are important, and how and for what purpose they are used. The consortium will then publish the data and offer analytic sensitivity recommendations where appropriate. These mandates will be conducted in collaboration and coordination with clinical laboratories, external quality assurance programs, and pathology organizations. DATA SOURCES.­: Literature review and published external quality assurance data. CONCLUSIONS.­: Integration of analytic standards is expected to (1) harmonize and standardize IHC assays; (2) improve IHC test accuracy and reproducibility, both within and between laboratories; and (3) dramatically simplify and improve methodology transfer for new IHC protocols from published literature or clinical trials to clinical IHC laboratories.

Avoiding "False Positive" and "False Negative" Immunohistochemical Results in Breast Pathology.

Immunohistochemistry (IHC) plays an important role in the evaluation of breast pathology specimens to provide both diagnostic and prognostic/therapeutic information. Although most IHCs used in breast pathology can be easily interpreted, pitfalls do exist, especially in some uncommon scenarios. This review intends to focus on the challenging areas such as the interpretation of myoepithelial cell markers in differentiating benign proliferation and in situ carcinoma from invasive carcinoma, lobular cell markers in differentiating lobular from ductal carcinoma, cytokeratin and other markers in diagnosing metaplastic carcinoma, and breast tissue origin markers in diagnosing breast primary carcinoma. The challenges in interpreting prognostic and predictive markers will be also discussed.

Prognostic Utility of Breast Cancer Index to Stratify Distant Recurrence Risk in Invasive Lobular Carcinoma.

PURPOSE: The prognostic utility of Breast Cancer Index (BCI) for risk assessment of overall (0-10 years), early (0-5 years), and late (5-10 years) distant recurrence (DR) in hormone receptor-positive (HR+) invasive lobular carcinoma (ILC) was evaluated. EXPERIMENTAL DESIGN: BCI gene expression analysis was performed blinded to clinical outcome utilizing tumor specimens from patients with HR+ ILC from a multi-institutional cohort. The primary endpoint was time to DR. Kaplan-Meier analyses of overall, early, and late DR risk were performed, and statistical significance was evaluated by log-rank test and Cox proportional hazards regression. The prognostic contribution of BCI in addition to clinicopathologic factors was evaluated by likelihood ratio analysis. RESULTS: Analysis of 307 patients (99% ER+, 53% T1, 42% N+, 70% grade II) showed significant differences in DR over 10 years based on BCI risk categories. BCI low- and intermediate-risk patients demonstrated similar DR rates of 7.6% and 8.0%, respectively, compared with 27.0% for BCI high-risk patients. BCI was a significant independent prognostic factor for overall 10-year DR [HR = 4.09; 95% confidence interval (CI), 2.00-8.34; P = 0.0001] as well as for both early (HR = 8.19; 95% CI, 1.85-36.30; P = 0.0042) and late (HR = 3.04; 95% CI, 1.32-7.00; P = 0.0224) DR. In multivariate analysis, BCI remained the only statistically significant prognostic factor for DR (HR = 3.49; 95% CI, 1.28-9.54; P = 0.0150). CONCLUSIONS: BCI is an independent prognostic factor for ILC and significantly stratified patients for cumulative risk of 10-year, early, and late DR. BCI added prognostic value beyond clinicopathologic characteristics in this distinct subtype of breast cancer.

Determining breast cancer biomarker status and associated morphological features using deep learning.

Background: Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and well-validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker findings can prompt repeat testing to address biological, interpretative, or technical reasons for unexpected results. Methods: We developed three independent deep learning systems (DLS) to directly predict ER/PR/HER2 status for both focal tissue regions (patches) and slides using hematoxylin-and-eosin-stained (H&E) images as input. Models were trained and evaluated using pathologist annotated slides from three data sources. Areas under the receiver operator characteristic curve (AUCs) were calculated for test sets at both a patch-level (>135 million patches, 181 slides) and slide-level (n = 3274 slides, 1249 cases, 37 sites). Interpretability analyses were performed using Testing with Concept Activation Vectors (TCAV), saliency analysis, and pathologist review of clustered patches. Results: The patch-level AUCs are 0.939 (95%CI 0.936-0.941), 0.938 (0.936-0.940), and 0.808 (0.802-0.813) for ER/PR/HER2, respectively. At the slide level, AUCs are 0.86 (95%CI 0.84-0.87), 0.75 (0.73-0.77), and 0.60 (0.56-0.64) for ER/PR/HER2, respectively. Interpretability analyses show known biomarker-histomorphology associations including associations of low-grade and lobular histology with ER/PR positivity, and increased inflammatory infiltrates with triple-negative staining. Conclusions: This study presents rapid breast cancer biomarker estimation from routine H&E slides and builds on prior advances by prioritizing interpretability of computationally learned features in the context of existing pathological knowledge.

Immunohistochemical Markers With Potential Diagnostic, Prognostic, and Therapeutic Significance in Uterine Carcinosarcoma: A Clinicopathologic Study of 43 Cases.

Uterine carcinosarcomas (UCS) are rare and highly aggressive tumors. Although it is currently accepted that the majority of UCS are metaplastic carcinomas, their aggressive behavior is unparalleled to that of any other high-grade endometrial neoplasms. Therefore, the search for the distinct immunohistochemical and molecular features that could help in the development of new treatment strategies continues. We evaluated the expression of PDL-1, growth hormone releasing hormone receptor, p53, WT1, PAX-8, estrogen receptor, HNF-1, and mismatch repair proteins in 43 UCS. Tumors were selected from the archives of the Magee-Womens Hospital University of Pittsburgh Medical Center Department of Pathology. Seventeen were stage I, 4 were stage II, 15 were stage III, and 7 were stage IV. The median age was 67 yr and median overall survival was 3.2 yr. Immunostaining for PAX8, HNF-1, and estrogen receptor showed statistically significant difference between epithelial and stromal components. Expression of p53 was significantly associated with clinical high stage, but other markers did not correlate with stage or survival. Immunostaining for programmed death ligand-1 was strongly positive in 30 UCS (70%), including 24 cases with tumor cell positivity, 12 cases with tumor cell and tumor-infiltrating immune cell positivity, and 6 cases with tumor-infiltrating immune cell positivity only. Of 27 tumors tested for mismatch repair expression, 12 (44%) showed loss of expression, 7 of which were PDL-1 positive. Growth hormone releasing hormone receptor was positive in 38 tumors (88%) and predominantly expressed in the epithelial component. The range of positivity for programmed death ligand-1 and growth hormone releasing hormone receptor suggests a possible potential adjuvant treatment that may be considered for UCS.

Magee Equations™ and response to neoadjuvant chemotherapy in ER+/HER2-negative breast cancer: a multi-institutional study.

Magee Equations™ (ME) are multivariable models that can estimate oncotype DX® recurrence score. One of the equations, Magee Equation 3 (ME3) which utilizes only semi-quantitative receptor results has been shown to provide chemopredictive value in the neoadjuvant setting in a single institutional study. This multi-institutional study (seven institutions contributed cases) was undertaken to examine the validity of ME3 in predicting response to neoadjuvant chemotherapy in estrogen receptor positive, HER2-negative breast cancers. Stage IV cases were excluded. The primary endpoint was the pathologic complete response (pCR) rate in different categories of ME3 scores calculated based on receptor results in the pre-therapy core biopsy. A total of 166 cases met the inclusion criteria. The patient age ranged from 24 to 83 years (median 53 years). The average pre-therapy tumor size was 3.9 cm, and axillary lymph nodes were confirmed positive by pre-therapy core biopsy in 85 of 166 cases (51%). The pCR rate according to ME3 scores was 0% (0 of 64) in ME3 < 18, 0% (0 of 46) in ME3 18-25, 14% (3 of 21) in ME3 > 25 to <31, and 40% (14 of 35) in ME3 score 31 or higher (p value: <0.0001). There were no distant recurrences and no deaths in the 17 patients with pCR. In the remaining 149 cases with residual disease, ME3 score of >25 was significantly associated with shorter distant recurrence-free survival and showed a trend for shorter breast cancer-specific survival. The results of this multi-institutional study are similar to previously published data from a single institution (PMID: 28548119) and confirm the chemo-predictive value of ME3 in the neoadjuvant setting. In addition, ME3 may provide prognostic information in patients with residual disease which should be further evaluated.

Prosigna® breast cancer assay: histopathologic correlation, development, and assessment of size, nodal status, Ki-67 (SiNK™) index for breast cancer prognosis.

The Prosigna® assay is a United States Food and Drug Administration (US-FDA) cleared molecular test for prognostic use in hormone receptor-positive stage I/II breast cancer in postmenopausal women. We analyzed histopathologic features of 79 cases with Prosigna® assay results and found a significant correlation between tumor size, grade, and Ki-67 labeling index with Prosigna® score (0-40, 41-60, and 61-100) and Prosigna® risk categories. Since the Prosigna® risk stratification is influenced by lymph node status, we designed an index that included lymph node status and the two most correlated variables (size and Ki-67 labeling index). This was termed the size, nodal, and Ki-67 (SiNK™) index and is calculated as follows: (size in mm) + (pN × 10) + (Ki-67 labeling index). The SiNK™ index was divided into ≤40 and >40 to test its prognostic significance in a well-characterized dataset of 106 ER+/HER2-negative stage I-II invasive breast cancers treated with standard multi-modality therapy with long term follow-up (average 101 months follow-up). Patients with SiNK™ ≤40 showed significantly improved distant recurrence-free survival (96% distant recurrence-free survival in SiNK™ ≤40 compared to 81% in SiNK™ >40; log-rank test p value: 0.0027). SiNK™ provides strong prognostic information in ERo+/HER2-negative breast cancers. SiNK™ index is simple to calculate using data from routine pathology reports. This should be further evaluated in larger datasets.

Best Practice (Efficient) Immunohistologic Panel for Diagnosing Metaplastic Breast Carcinoma.

Immunohistochemistry (IHC) plays a key role in the diagnosis of metaplastic breast carcinomas (MBCs), particularly the spindle cell variant. The most efficient immunopanel has yet to be developed. We studied the immunoprofile of 45 MBCs including 23 matrix-producing MBCs, 11 squamous cell carcinomas, 6 spindle cell carcinomas, and 5 mixed-subtypes (2 cases including spindle cell components). Representative sections from mastectomy or core biopsy specimens were subject to IHC using a list of antibodies including OSCAR, a recently developed antibody against pooled cytokeratins. The staining was interpreted as positive when >1% of tumor cells demonstrated unequivocal staining. As a result, OSCAR showed similar sensitivity to AE1/AE3 and CAM 5.2 (89.1% vs. 89.4% vs. 89.4%) for MBCs, but the former showed more diffuse pattern of staining, particularly in spindle cell carcinomas. High molecular weight cytokeratin CK14, CK5, and CK17 were positive in 91.3%, 87.2%, and 73.3% of MBCs, respectively. CK7 was much less likely to be positive in spindle cell carcinomas (37.5%) than in other variants of MBCs (97.4%). P63 and CK14 were the most useful markers for spindle cell carcinomas, positive in 87.5% and 85.7% of cases, respectively. GATA 3 was positive in 63% MBCs, and nonspecific staining for vimentin and smooth muscle actin were common. Random combination of up to 3 antibodies against keratins including p63 showed sensitivities ranging from 80.9% to 97.9%. Our results suggested the combination of OSCAR, CK14 and p63 is the most efficient panel (sensitivity 97.9%) for diagnosing MBCs.

Metaplastic carcinomas of the breast without evidence of epithelial differentiation: a diagnostic approach for management.

AIMS: Although rare, malignant sarcomatoid breast tumours without evidence of epithelial differentiation comprise a diagnostic challenge with management implications. Earlier studies have generally considered these to be primary breast sarcomas; however, supporting evidence is lacking and management remains variable. This study aimed to provide an evidence-based approach to improve the consistency of diagnosis and management for such cases. METHODS AND RESULTS: A large series (n = 140) of metaplastic breast carcinoma (MBC) diagnosed in Nottingham over 18 years was analysed. Only cases with available data on immunohistochemical expression of cytokeratins (CKs) were included. The prevalence and pattern of expression for various CKs were assessed and details of tumours negative for CKs were collected. A diagnostic approach based on our experience is provided. Forty-seven cases (34%) showed foci of conventional type invasive breast carcinoma or ductal carcinoma in situ (DCIS), while 93 cases (66%) were diagnosed as MBC based on morphology and/or CK expression. Ninety-seven cases (69%) were negative for one or more CKs, with 18 cases (13%) negative for five or more CKs. Eight cases (6%) lacked expression of all CKs tested. Further examination showed evidence of carcinomatous nature in five cases, and three were diagnosed as MBC following extensive diagnostic work-up and based on our experience. CONCLUSION: This study suggests that MBC represents a spectrum of neoplasms, with some lacking CK expression. Sarcomatoid neoplasms of the breast lacking evidence of carcinomatous morphology and CK expression may represent an extreme end of differentiation that can be considered as carcinomas rather than sarcomas for management purposes (following extensive work-up).

Differential Regulation and Targeting of Estrogen Receptor α Turnover in Invasive Lobular Breast Carcinoma.

Invasive lobular breast carcinoma (ILC) accounts for 10% to 15% of breast cancers diagnosed annually. Evidence suggests that some aspects of endocrine treatment response might differ between invasive ductal carcinoma (IDC) and ILC, and that patients with ILC have worse long-term survival. We analyzed The Cancer Genome Atlas dataset and observed lower levels of ESR1 mRNA (P = 0.002) and ERα protein (P = 0.038) in ER+ ILC (n = 137) compared to IDC (n = 554), and further confirmed the mRNA difference in a local UPMC cohort (ILC, n = 143; IDC, n = 877; P < 0.005). In both datasets, the correlation between ESR1 mRNA and ERα protein was weaker in ILC, suggesting differential post-transcriptional regulation of ERα. In vitro, 17ß-estradiol (E2) decreased the rate of degradation and increased the half-life of ERα in ILC cell lines, whereas the opposite was observed in IDC cell lines. Further, E2 failed to induce robust ubiquitination of ERα in ILC cells. To determine the potential clinical relevance of these findings, we evaluated the effect of 2 selective estrogen receptor downregulators (SERDs), ICI 182,780 and AZD9496, on ERα turnover and cell growth. While ICI 182,780 and AZD9496 showed similar effects in IDC cells, in ILC cell lines, AZD9496 was not as effective as ICI 182,780 in decreasing ERα stability and E2-induced proliferation. Furthermore, AZD9496 exhibited partial agonist activity in growth assays in ILC cell lines. Our study provides evidence for a distinct ERα regulation by SERDs in ILC cell lines, and therefore it is important to include ILC models into preclinical and clinical testing of novel SERDs.

The healthcare value of the Magee Decision Algorithm™: use of Magee Equations™ and mitosis score to safely forgo molecular testing in breast cancer.

Magee Equations™ are multivariable models that can estimate oncotype DX® Recurrence Score, and Magee Equation 3 has been shown to have chemopredictive value in the neoadjuvant setting as a standalone test. The current study tests the accuracy of Magee Decision Algorithm™ using a large in-house database. According to the algorithm, if all Magee Equation scores are <18, or 18-25 with a mitosis score of 1, then oncotype testing is not required as the actual oncotype recurrence score is expected to be ≤25 (labeled "do not send"). If all Magee Equation scores are 31 or higher, then also oncotype testing is not required as the actual score is expected to be >25 (also "do not send"). All other cases could be considered for testing (labeled "send"). Of the 2196 ER+, HER2-negative cases sent for oncotype testing, 1538 (70%) were classified as "do not send" and 658 (30%) as "send". The classification accuracy in the "do not send" group was 95.1%. Of the 75 (4.9%) discordant cases (expected score ≤25 by decision algorithm but the actual oncotype score >25), 26 received endocrine therapy alone. None of these 26 patients experienced distant recurrence (average follow-up of 73 months). The Magee Decision Algorithm accurately identifies cases that will not benefit from oncotype testing. Such cases constitute ~70% of the routine clinical oncotype requests, an estimated saving of $300,000 per 100 test requests. The occasional discordant cases (expected ≤25, but actual oncotype score >25) appears to have an excellent outcome on endocrine therapy alone.